import sys
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_theme()
results_folder = 'mmvec_major_taxa_scrambled_2'
results_base_name = 'latent_dim_3_input_prior_1.00_output_prior_1.00_beta1_0.90_beta2_0.95'
table = pd.read_table(results_folder + '/' + results_base_name + '_ranks.txt', index_col=0)
table.head()
| Propionibacteriaceae | Staphylococcus caprae or capitis | Staphylococcus epidermidis | Staphylococcus hominis | Other Staphylococci | Polyomavirus HPyV6 | Polyomavirus HPyV7 | Merkel Cell Polyomavirus | Malasseziaceae | Corynebacteriaceae | Micrococcaceae | Other families | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| featureid | ||||||||||||
| X940001 | 0.184421 | 0.053692 | 0.153110 | 0.199588 | -0.032911 | -0.020608 | -0.037587 | 0.110812 | 0.084744 | -0.016153 | 0.145283 | -0.061960 |
| X940002 | -0.070998 | -0.077200 | -0.037909 | -0.008117 | -0.079228 | -0.080866 | -0.150118 | -0.134636 | -0.085292 | -0.077377 | -0.008113 | -0.081268 |
| X940005 | -0.020409 | -0.113338 | -0.017493 | -0.000518 | -0.116096 | -0.107599 | -0.377870 | -0.157181 | -0.151141 | -0.094980 | 0.057534 | -0.135563 |
| X940007 | 0.095343 | 0.247376 | 0.343342 | 0.376841 | 0.278461 | 0.203163 | 0.406508 | 0.115586 | 0.326311 | 0.269885 | 0.322992 | 0.287431 |
| X940010 | 1.102840 | 0.815784 | -0.069650 | 0.356785 | 0.513313 | 1.011083 | 0.652279 | 0.737868 | 0.371052 | 0.363324 | 0.048634 | 0.631470 |
table['Selected'] = np.isin(table.index,
['X940203', 'X940589', 'X940625', 'X940925', 'X940936', 'X942191',
'X942237', 'X950023', 'X950028', 'X950056', 'X950157', 'X950173',
'X950193', 'X950225', 'X950228', 'X950233', 'X950254', 'X950396',
'X950485', 'X950584', 'X950661', 'X950999', 'X960035', 'X960242',
'X960306', 'X960421', 'X960463', 'X960465', 'X960712', 'X960726',
'X960934', 'X961553', 'X961686', 'X970018', 'X970091', 'X970092',
'X970232', 'X970283', 'X970327', 'X970342', 'X970633', 'X970680']
)
table.sort_values('Selected', inplace=True)
sns.relplot(
table,
y='Propionibacteriaceae', x='Staphylococcus epidermidis', hue='Selected'
)
<seaborn.axisgrid.FacetGrid at 0x7f478b89b3d0>
sns.pairplot(table, hue='Selected')
<seaborn.axisgrid.PairGrid at 0x7f478b40dbd0>
for i in table.columns[:-1]:
sns.displot(table, x=i, hue='Selected', multiple='stack')